How do you secure ChatGPT in the enterprise?
Secure ChatGPT Enterprise with five checks: managed account access, provider data-handling settings, rules for sensitive prompts and uploads, permissions for connected apps, and reviewable activity. OpenAI does not train on business data by default. That commitment is separate from your organisation’s decision about which data employees may submit and which actions require an additional control. OpenAI enterprise privacy
Test one policy before rollout. Using a fictional customer record, compare an allowed prompt with a sensitive prompt and file upload in the intended ChatGPT account and client. Check the configured response and recorded event. Aona adds blocking or redaction on supported interactions and evidence for the security review. Evaluate DLP for ChatGPT · Verify an AI policy control
Introduction: The ChatGPT Challenge for Enterprise Security
Employees may use ChatGPT through approved workspaces or personal accounts, with different data terms and controls. Start by identifying the accounts, devices and information involved, then decide which uses your organisation can permit.
This guide explains how to assess ChatGPT security risks, set workplace rules, apply technical controls and monitor approved use.
Understanding the Security Risks of ChatGPT in Enterprise
Before crafting a response, security teams must understand the specific risks ChatGPT introduces. These risks fall into several categories, each requiring different controls.
Data Leakage and Confidentiality
Sensitive prompts and uploads can include customer records, source code and confidential documents. OpenAI’s enterprise privacy commitments cover model training, retention, access and connected apps. Review each separately for your workspace: a no-training commitment does not by itself mean zero retention or permission to share every data type. Read OpenAI’s enterprise privacy commitments
An employee sending a confidential customer record to an unapproved account can create an incident even when the provider has strong security controls. Use synthetic records to test the policy and involve the responsible owner when real information may have been disclosed.
Compliance and Regulatory Risks
Before processing personal or regulated data in ChatGPT, identify the applicable legal basis, contract, security safeguards and any required impact assessment. Requirements depend on the data, jurisdiction and use case; consent is not the only possible legal basis. Have the responsible privacy or legal owner approve the intended use. Review the AI data processing agreement template
The EU AI Act adds additional obligations for organisations deploying AI in high-risk contexts, including transparency requirements that are difficult to meet with third-party AI services.
Accuracy and Hallucination Risks
ChatGPT can generate confident but incorrect information, a phenomenon known as hallucination. In enterprise contexts, this translates to business risk: incorrect legal advice, flawed financial analysis, inaccurate customer communications, or faulty technical recommendations. Without verification processes, AI-generated content can propagate errors through the organisation.
Intellectual Property Concerns
Content generated by ChatGPT raises intellectual property questions. Can you copyright AI-generated content? Are you inadvertently using others' copyrighted material? Could your proprietary information appear in other users' outputs? These questions remain legally unsettled in most jurisdictions, creating risk for organisations that rely heavily on AI-generated content.
Shadow AI and Visibility Gaps
Personal accounts and unmanaged devices can leave gaps in workspace reporting. Combine approved-account administration with device controls and employee guidance. Aona visibility requires its browser plugin or native app on the device; a risk-catalogue entry does not establish visibility into an unmanaged device.
Building a ChatGPT Workplace Policy
An effective ChatGPT policy balances security with usability. Overly restrictive policies drive usage underground; permissive policies leave the organisation exposed.
Policy Structure
- Scope: Define which AI tools are covered (ChatGPT, Claude, Gemini, Copilot, and others).
- Classification: Categorise usage into approved, conditionally approved, and prohibited activities.
- Data handling rules: Specify what data types can and cannot be shared with AI tools (use your existing data classification scheme).
- Approved platforms: List sanctioned AI tools and approved access methods (enterprise accounts vs. personal accounts).
- Verification requirements: Mandate human review for AI-generated content in specified contexts.
- Incident reporting: Require reporting of accidental data exposure or policy violations.
Example Policy Categories
Approved: Using ChatGPT Enterprise for drafting marketing copy, brainstorming ideas, or summarising public information.
Conditionally Approved: Using AI for code assistance with non-proprietary code, with review before committing.
Prohibited: Sharing customer PII, financial data, source code, legal documents, or strategic plans with any external AI tool.
Use the editable policy to record approved accounts, permitted data and reporting contacts. Download the AI acceptable use policy template
Implementing Technical Controls
Policy alone is not enough. Technical controls provide enforcement and visibility that make policies effective.
AI-Specific Data Loss Prevention (DLP)
Compare the actions your existing DLP actually inspects with the ChatGPT prompts and uploads you need to protect. Some endpoint, browser and network products already inspect supported AI submissions. Verify the licence, client, input path and policy response before adding a control. Microsoft Purview browser DLP scope
Key capabilities to look for: real-time content inspection, AI service detection and categorisation, granular policy enforcement by user role and data type, and comprehensive audit logging.
Enterprise AI Platforms
ChatGPT Enterprise provides managed workspace access, encryption and supported audit logging, with no training on business data by default. Confirm retention, connected-app permissions and administration features for the plan you use. Keep these provider controls distinct from pre-submission protection of a particular prompt or file. OpenAI enterprise privacy and security · Review Aona deployment and data handling
Network and Access Controls
- Block access to consumer AI services from corporate networks and devices.
- Route approved AI usage through managed enterprise platforms.
- Implement CASB (Cloud Access Security Broker) rules for AI services.
- Monitor DNS and web traffic for unsanctioned AI tool access.
Workforce AI Security
Aona combines AI-use visibility, policy enforcement and sensitive-data protection for employees. Deploy its browser plugin or native app on managed devices, then verify the supported application and input action. Keep account administration, policy approval and legal decisions with their respective owners.
Compare the leading solutions in our AI Governance Tool Comparison Guide.
Monitoring and Detection
Continuous monitoring is essential for maintaining security posture and demonstrating compliance.
What to Monitor
- AI tool usage patterns: Who is using which tools, how frequently, and for what purposes.
- Data flow to AI services: Volume and sensitivity of data being shared with external AI.
- Policy violations: Real-time alerts for prohibited activities.
- New AI tool adoption: Detection of new, unsanctioned AI services appearing in your environment.
- API usage: Monitoring programmatic access to AI services from internal systems.
Building an AI Security Dashboard
Create a centralised dashboard that provides security leadership with real-time visibility into AI risk posture. Key metrics include: total AI tools in use (sanctioned vs. unsanctioned), data sensitivity exposure score, policy compliance rate, incident trends, and user adoption patterns.
Training and Awareness
Prompt and upload controls can intervene before a supported submission reaches an AI service. Training complements those controls by helping employees choose approved accounts, recognise sensitive information and report mistakes. An effective programme should cover the following areas.
- What data is safe to share: Practical examples relevant to each department, not abstract policies.
- How to use AI safely: Prompt engineering techniques that avoid exposing sensitive data.
- How to verify AI outputs: Critical thinking skills for evaluating AI-generated content.
- How to report concerns: Simple, blame-free reporting channels for accidental data exposure.
- Regulatory context: Why AI governance matters for the organisation and its customers.
Repeat training quarterly and update content as tools and regulations evolve. Use real-world examples and anonymised internal incidents to make training relevant and engaging.
Secure Alternatives and Approved Workflows
Rather than simply restricting ChatGPT, offer employees secure alternatives that meet their productivity needs.
- ChatGPT Enterprise: a managed workspace with enterprise access, privacy and administration controls. Confirm the features and terms needed for your intended use.
- Azure-hosted AI services: review deployment region, supported models, access controls and data-handling terms for the selected service.
- Private LLM deployments: Self-hosted models for the most sensitive use cases.
- Domain-specific AI tools: Approved, purpose-built AI tools for common tasks (code completion, writing assistance, data analysis).
Create approved workflow templates for common use cases: email drafting, code review, data analysis, content creation, and customer service. These templates include pre-approved prompts, data handling guidelines, and verification steps.
For more on secure AI deployment options, explore our AI Security Guides.
Take Control of ChatGPT in Your Organisation
Clear policies, technical controls, monitoring and training help organisations manage employee use of ChatGPT. Reassess the approved accounts and data rules when provider terms, tools or business needs change.
- Start with our AI Acceptable Use Policy Template for immediate policy deployment.
- Explore the AI Governance Glossary for clear definitions of key terms.
- Compare enterprise AI platforms in our Solution Comparison Guide.
Bring one ChatGPT account, data category and employee action to an Aona evaluation. Agree the supported client, synthetic test and evidence needed before rollout. This control guidance and the linked OpenAI privacy commitments were reviewed on 2 November 2026. Scope a ChatGPT security demo · Define evaluation acceptance criteria
